library(sp)
library(spdep)
library(dplyr)
library(rstan)
library(loo)
library(ggplot2)
library(bayesplot)
library(glmnet)
Error in library(glmnet) : there is no package called ‘glmnet’
GM@data <- GM@data %>%
mutate(
log_pm25 = log(pm25),
log_sat = log(sat_2014)
)
What variables are available in this data set?
print(colnames(GM@data))
[1] "City_locality" "iso3" "country"
[4] "super_region" "super_region_name" "pm25"
[7] "sat_2014" "log_pm25" "log_sat"
Regression on the original values instead of in log-domain
xylabs <- labs(
x = expression(satellite),
y = expression(PM[2.5])
)
region_color_manual = scale_color_manual(
values =
c("E-Eur/C-Eur/C-Asia" = "#00C094",
"HighIncome" = "#FB61D7",
"LatAm/Carib" = "#53B400",
"N-Afr/MidEast" = "#A58AFF",
"S-Asia" = "#00B6EB",
"SE-Asia/E-Asia/Oceania" = "#C49A00",
"Sub-Saharan Afr" = "#F8766D")
)
plot1 <- ggplot(GM@data, aes(y = pm25, x = sat_2014)) +
geom_point(aes(color = super_region_name), alpha = 0.4, size = rel(0.8)) +
region_color_manual +
geom_smooth(method = lm, color = "black", size = 0.5, linetype = 2) +
coord_equal() +
xylabs +
guides(color = guide_legend(
title = NULL,
override.aes = list(alpha = 1, size = 2)
)) +
theme(legend.text = element_text(size = rel(0.6)))
plot(plot1)

Fitting a second degree polynomial instead
xylabs <- labs(
x = expression(log(satellite)),
y = expression(log(PM[2.5]))
)
region_color_manual = scale_color_manual(
values =
c("E-Eur/C-Eur/C-Asia" = "#00C094",
"HighIncome" = "#FB61D7",
"LatAm/Carib" = "#53B400",
"N-Afr/MidEast" = "#A58AFF",
"S-Asia" = "#00B6EB",
"SE-Asia/E-Asia/Oceania" = "#C49A00",
"Sub-Saharan Afr" = "#F8766D")
)
plot1 <- ggplot(GM@data, aes(y = log_pm25, x = log_sat)) +
geom_point(aes(color = super_region_name), alpha = 0.4, size = rel(0.8)) +
region_color_manual +
geom_smooth(method = glm, formula=y~poly(x, 2), color = "black", size = 0.5, linetype = 2) +
coord_equal() +
xylabs +
guides(color = guide_legend(
title = NULL,
override.aes = list(alpha = 1, size = 2)
)) +
theme(legend.text = element_text(size = rel(0.6)))
plot(plot1)

Model used in the paper
xylabs <- labs(
x = expression(log(satellite)),
y = expression(log(PM[2.5]))
)
region_color_manual = scale_color_manual(
values =
c("E-Eur/C-Eur/C-Asia" = "#00C094",
"HighIncome" = "#FB61D7",
"LatAm/Carib" = "#53B400",
"N-Afr/MidEast" = "#A58AFF",
"S-Asia" = "#00B6EB",
"SE-Asia/E-Asia/Oceania" = "#C49A00",
"Sub-Saharan Afr" = "#F8766D")
)
plot1 <- ggplot(GM@data, aes(y = log_pm25, x = log_sat)) +
geom_point(aes(color = super_region_name), alpha = 0.4, size = rel(0.8)) +
region_color_manual +
geom_smooth(method = lm, color = "black", size = 0.5, linetype = 2) +
coord_equal() +
xylabs +
guides(color = guide_legend(
title = NULL,
override.aes = list(alpha = 1, size = 2)
)) +
theme(legend.text = element_text(size = rel(0.6)))
plot(plot1)

r1 <- subset(GM@data, super_region==1)
r2 <- subset(GM@data, super_region==2)
r3 <- subset(GM@data, super_region==3)
r4 <- subset(GM@data, super_region==4)
r5 <- subset(GM@data, super_region==5)
r6 <- subset(GM@data, super_region==6)
r7 <- subset(GM@data, super_region==7)
b1 <- lm(r1$log_pm25 ~ r1$log_sat)
print(b1$coefficients)
(Intercept) r1$log_sat
1.2928677 0.4628297
b2 <- lm(r2$log_pm25 ~ r2$log_sat)
print(b2$coefficients)
(Intercept) r2$log_sat
1.424214 0.700595
b3 <- lm(r3$log_pm25 ~ r3$log_sat)
print(b3$coefficients)
(Intercept) r3$log_sat
0.4585961 0.9131791
b4 <- lm(r4$log_pm25 ~ r4$log_sat)
print(b4$coefficients)
(Intercept) r4$log_sat
1.0401566 0.6807239
b5 <- lm(r5$log_pm25 ~ r5$log_sat)
print(b5$coefficients)
(Intercept) r5$log_sat
2.3130627 0.2960964
b6 <- lm(r6$log_pm25 ~ r6$log_sat)
print(b6$coefficients)
(Intercept) r6$log_sat
1.8787025 0.5071027
b7 <- lm(r7$log_pm25 ~ r7$log_sat)
print(b7$coefficients)
(Intercept) r7$log_sat
2.9589930 0.1482167
plot_r1 <- ggplot(r3, aes(y = log_pm25, x = log_sat)) +
geom_point(aes(colour = super_region_name), alpha = 0.2, size = rel(0.75)) +
region_color_manual +
geom_smooth(aes(colour = super_region_name), method = lm) +
coord_equal() +
xylabs +
legend_none()
plot(plot_r1)

Same plot as above + separate fits for each super-region
plot2 <- ggplot(GM@data, aes(y = log_pm25, x = log_sat)) +
geom_point(aes(colour = super_region_name), alpha = 0.2, size = rel(0.75)) +
geom_smooth(method = lm, color = "black", size = 0.5, linetype = 2) +
region_color_manual +
geom_smooth(aes(colour = super_region_name), method = lm) +
coord_equal() +
xylabs +
legend_none()
plot(plot2)

Derive labels from the data based on average PM2.5 concentration in each country
average <-
GM@data %>%
group_by(iso3) %>%
summarise(pm25 = mean(pm25))
d <- dist(average)
NAs introduced by coercion
hh <- hclust(d)
clust <- cutree(hh,k = 6)
GM@data$cluster_region <-
sapply(GM@data$iso3, function(x) clust[which(average$iso3 == x)])
print(table(GM@data$cluster_region))
1 2 3 4 5 6
14 2172 400 376 10 8
cluster_color_manual = scale_color_manual(
values =
c("4" = "#00C094",
"1" = "#FB61D7",
"5" = "#53B400",
"2" = "#A58AFF",
"3" = "#00B6EB",
"6" = "#C49A00",
"7" = "#F8766D")
)
plot3 <-
ggplot(GM@data, aes(
y = log_pm25,
x = log_sat
)) +
geom_point(aes(colour = as.factor(cluster_region)), alpha = 0.2, size = rel(0.75)) +
cluster_color_manual +
geom_smooth(
method = lm,
color = "black",
size = 0.5,
linetype = 2
) +
geom_smooth(aes(colour = as.factor(cluster_region)), method = lm) +
coord_equal() +
guides(color = guide_legend(
title = NULL,
override.aes = list(alpha = 1, size = 2)
)) +
xylabs
plot(plot3)
# Prior predictive simulations --------------------------------------------
# Inverse Gamme distribution.
library(gridExtra)
library(grid)
library(invgamma)
library(ggplot2); theme_set(theme_bw())
x <- seq(0, 500, .01)
qplot(x, dinvgamma(x, 1, 1), geom = "line") -> plot1
qplot(x, dinvgamma(x, 1, 100), geom = "line") -> plot2
#qplot(x, 1/dinvgamma(x, 1, 1), geom = "line") -> plot3
#qplot(x, 1/dinvgamma(x, 1, 100), geom = "line") -> plot4
#qplot(x, dinvgamma(x, 2, 100), geom = "line") -> plot3
#qplot(x, dinvgamma(x, 4, 100), geom = "line") -> plot4
grid.arrange(plot1, plot2, ncol = 2)

# Plot: prior predictive with vague priors
set.seed(seed = 1923840483)
tau0 <- 1 / sqrt(rgamma(1, 1, rate = 100))
tau1 <- 1 / sqrt(rgamma(1, 1, rate = 100))
sigma <- 1 / sqrt(rgamma(1, 1, rate = 100))
beta0i <- rnorm(8, 0, tau0)
beta1i <- rnorm(8, 0, tau1)
beta0 <- rnorm(1, 0, 100)
beta1 <- rnorm(1, 0, 100)
Nsim <- length(GM@data$super_region)
xysim_labs <- labs(
x = expression(paste("Observed ", log(PM[2.5]))),
y = "Simulated data (log scale)"
)
data1 <- data.frame(
log_pm25 = GM$log_pm25,
sim = beta0 + beta0i[GM$super_region] +
(beta1 + beta1i[GM$super_region]) * GM$log_sat +
rnorm(Nsim, mean = 0, sd = sigma)
)
theme_set(bayesplot::theme_default(base_size = 18))
theme_update(axis.text = element_text(size = 20))
ggplot(data1, aes(x = log_pm25, y = sim)) +
geom_point(alpha = 0.1, color = "red") +
xysim_labs + ggtitle("Vague Recommended Priors")
ggsave(filename = "plots/prior_pred_vague.png", width = 4.5, height = 3.75)

# Plot: prior predictive with weakly informative priors
set.seed(seed = 1923840479)
tau0 <- abs(rnorm(1, 0, 1))
tau1 <- abs(rnorm(1, 0, 1))
sigma <- abs(rnorm(1, 0, 1))
beta0i <- rnorm(8, 0, tau0)
beta1i <- rnorm(8, 0, tau1)
beta0 <- rnorm(1, 0, 1)
beta1 <- rnorm(1, 1, 1)
data2 <- data.frame(
log_pm25 = GM$log_pm25,
sim = beta0 + beta0i[GM$super_region] +
(beta1 + beta1i[GM$super_region]) * GM$log_sat +
rnorm(Nsim, mean = 0, sd = sigma)
)
ggplot(data2, aes(x = log_pm25, y = sim)) +
geom_point(alpha = 0.1) +
xysim_labs + ggtitle("Weakly informative joint prior data generating process")
ggsave(filename = "plots/prior_pred_wip.png", width = 4.5, height = 3.75)

# Plot: prior predictive with weakly informative priors (no seed)
tau0 <- abs(rnorm(1, 0, 1))
tau1 <- abs(rnorm(1, 0, 1))
sigma <- abs(rnorm(1, 0, 1))
beta0i <- rnorm(8, 0, tau0)
beta1i <- rnorm(8, 0, tau1)
beta0 <- rnorm(1, 0, 1)
beta1 <- rnorm(1, 1, 1)
data2 <- data.frame(
log_pm25 = GM$log_pm25,
sim = beta0 + beta0i[GM$super_region] +
(beta1 + beta1i[GM$super_region]) * GM$log_sat +
rnorm(Nsim, mean = 0, sd = sigma)
)
ggplot(data2, aes(x = log_pm25, y = sim)) +
geom_point(alpha = 0.1) +
xysim_labs
ggsave(filename = "plots/prior_pred_wip.png", width = 4.5, height = 3.75)

# Plot: prior predictive comparison
data3 <- data.frame(
log_pm25 = GM$log_pm25,
wip = data2$sim,
vague = data1$sim
)
ggplot(data3, aes(x=log_pm25, y=wip)) +
geom_point(alpha = 0.1) +
geom_point(
aes(y = vague),
color = "red",
alpha = 0.1
) +
xysim_labs
ggsave(filename = "plots/prior_pred_compare.png", width = 4.5, height = 3.75)

# Fit Stan models 1, 2, 3 -------------------------------------------------
# Compile Stan programs
# * simple.stan: simple linear regression (Model 1)
# * hier.stan: non-centered parameterization of hierarchical model (Model 2, Model 3)
simple_mod <- stan_model("stan/simple.stan")
hier_mod <- stan_model("stan/hierarchical.stan")
# Data for model 1
standata1 <- with(GM@data, list(
N = length(log_pm25),
log_pm = log_pm25,
log_sat = log_sat
))
# Data for model 2 (using super-regions from WHO)
standata2 <- with(GM@data, list(
N = length(log_pm25),
R = length(unique(super_region)),
log_pm = log_pm25,
log_sat = log_sat,
region = super_region
))
# Data for model 3 (using super-regions from clustering)
standata3 <- standata2
standata3$R <- length(unique(GM$cluster_region))
standata3$region <- GM$cluster_region
# Fit the models with Stan
nuts_controls <- list(max_treedepth = 15, adapt_delta = 0.99)
mod1 <- sampling(simple_mod, data = standata1, seed = 2402)
SAMPLING FOR MODEL 'simple' NOW (CHAIN 1).
Gradient evaluation took 0.000554 seconds
1000 transitions using 10 leapfrog steps per transition would take 5.54 seconds.
Adjust your expectations accordingly!
Iteration: 1 / 2000 [ 0%] (Warmup)
Iteration: 200 / 2000 [ 10%] (Warmup)
Iteration: 400 / 2000 [ 20%] (Warmup)
Iteration: 600 / 2000 [ 30%] (Warmup)
Iteration: 800 / 2000 [ 40%] (Warmup)
Iteration: 1000 / 2000 [ 50%] (Warmup)
Iteration: 1001 / 2000 [ 50%] (Sampling)
Iteration: 1200 / 2000 [ 60%] (Sampling)
Iteration: 1400 / 2000 [ 70%] (Sampling)
Iteration: 1600 / 2000 [ 80%] (Sampling)
Iteration: 1800 / 2000 [ 90%] (Sampling)
Iteration: 2000 / 2000 [100%] (Sampling)
Elapsed Time: 2.52046 seconds (Warm-up)
2.69208 seconds (Sampling)
5.21255 seconds (Total)
SAMPLING FOR MODEL 'simple' NOW (CHAIN 2).
Gradient evaluation took 0.000181 seconds
1000 transitions using 10 leapfrog steps per transition would take 1.81 seconds.
Adjust your expectations accordingly!
Iteration: 1 / 2000 [ 0%] (Warmup)
Iteration: 200 / 2000 [ 10%] (Warmup)
Iteration: 400 / 2000 [ 20%] (Warmup)
# Graphical posterior predictive checks -----------------------------------
theme_set(bayesplot::theme_default(base_size = 14))
y <- standata2$log_pm
yrep1 <- as.matrix(mod1, pars = "log_pm_rep")
yrep2 <- as.matrix(mod2, pars = "log_pm_rep")
yrep3 <- as.matrix(mod3, pars = "log_pm_rep")
samp100 <- sample(nrow(yrep1), 100)
# overlaid densities
color_scheme_set("blue")
ppc_dens_overlay(y, yrep1[samp100, ]) +
coord_cartesian(ylim = c(0, 0.7), xlim = c(0, 6)) +
legend_none()
ggsave(filename = "plots/ppc_dens1.png", width = 4.5, height = 3.75)

color_scheme_set("gray")
ppc_dens_overlay(y, yrep2[samp100, ]) +
coord_cartesian(ylim = c(0, 0.7), xlim = c(0, 6)) +
legend_none()
ggsave(filename = "plots/ppc_dens2.png", width = 4.5, height = 3.75)

color_scheme_set("red")
ppc_dens_overlay(y, yrep3[samp100, ]) +
coord_cartesian(ylim = c(0, 0.7), xlim = c(0, 6)) +
legend_none()
ggsave(filename = "plots/ppc_dens3.png", width = 4.5, height = 3.75)

# stat: skew
skew <- function(x) {
xdev <- x - mean(x)
n <- length(x)
r <- sum(xdev^3) / sum(xdev^2)^1.5
return(r * sqrt(n) * (1 - 1/n)^1.5)
}
color_scheme_set("blue")
ppc_stat(y, yrep1, stat = "skew", binwidth = 0.01) +
xlim(0, .6) +
legend_none()
ggsave(filename = "plots/ppc_skew1.png", width = 4.5, height = 3.75)

color_scheme_set("gray")
ppc_stat(y, yrep2, stat = "skew", binwidth = 0.01) +
xlim(0, .6) +
legend_none()
ggsave(filename = "plots/ppc_skew2.png", width = 4.5, height = 3.75)

color_scheme_set("red")
ppc_stat(y, yrep3, stat = "skew", binwidth = 0.01) +
xlim(0, .6) +
legend_none()
ggsave(filename = "plots/ppc_skew3.png", width = 4.5, height = 3.75)

# stat: group medians
superregion <- GM@data$super_region_name
superregion <- factor(superregion, levels = levels(superregion)[c(2,1,3:7)])
color_scheme_set("blue")
ppc_stat_grouped(y, yrep1,
group = superregion,
stat = "median",
facet_args = list(nrow = 2)) +
facet_text(size = rel(0.7)) +
scale_x_continuous(breaks = function(x) pretty(x, n = 3)) +
legend_none()
ggsave(filename = "plots/ppc_med_grouped1.png", height = 3, width = 7)

color_scheme_set("gray")
ppc_stat_grouped(y, yrep2,
group = superregion,
stat = "median",
facet_args = list(nrow = 2)) +
facet_text(size = rel(0.7)) +
scale_x_continuous(breaks = function(x) pretty(x, n = 3)) +
legend_none()
ggsave(filename = "plots/ppc_med_grouped2.png", height = 3, width = 7)

color_scheme_set("red")
ppc_stat_grouped(y, yrep3,
group = standata3$region,
stat = "median",
facet_args = list(nrow = 2)) +
facet_text(size = rel(0.7)) +
scale_x_continuous(breaks = function(x) pretty(x, n = 3)) +
legend_none()
ggsave(filename = "plots/ppc_med_grouped3.png", height = 3, width = 7)

---
output: html_notebook
---

```{r}
library(sp)
library(spdep)
library(dplyr)
library(rstan)
library(loo)
library(ggplot2)
library(bayesplot)
library(glmnet)
theme_set(bayesplot::theme_default(base_size = 14))

# load 'GM' SpatialPointsDataFrame
load("bayes-vis.RData")
```


```{r}
GM@data <- GM@data %>% 
  mutate(
    log_pm25 = log(pm25), 
    log_sat = log(sat_2014)
  )
```


What variables are available in this data set?
```{r}
print(colnames(GM@data))
```

Regression on the original values instead of in log-domain
```{r}
xylabs <- labs(
  x = expression(satellite), 
  y = expression(PM[2.5])
)
region_color_manual = scale_color_manual(
    values = 
      c("E-Eur/C-Eur/C-Asia" = "#00C094",
        "HighIncome" = "#FB61D7",
        "LatAm/Carib" = "#53B400",
        "N-Afr/MidEast" = "#A58AFF",
        "S-Asia" = "#00B6EB",
        "SE-Asia/E-Asia/Oceania" = "#C49A00",
        "Sub-Saharan Afr" = "#F8766D")
  )

plot1 <- ggplot(GM@data, aes(y = pm25, x = sat_2014)) +
  geom_point(aes(color = super_region_name), alpha = 0.4, size = rel(0.8)) +
  region_color_manual +
  geom_smooth(method = lm, color = "black", size = 0.5, linetype = 2) +
  coord_equal() +
  xylabs +
  guides(color = guide_legend(
    title = NULL,
    override.aes = list(alpha = 1, size = 2)
  )) +
  theme(legend.text = element_text(size = rel(0.6)))
plot(plot1)
```

Fitting a second degree polynomial instead
```{r}
xylabs <- labs(
  x = expression(log(satellite)), 
  y = expression(log(PM[2.5]))
)
region_color_manual = scale_color_manual(
    values = 
      c("E-Eur/C-Eur/C-Asia" = "#00C094",
        "HighIncome" = "#FB61D7",
        "LatAm/Carib" = "#53B400",
        "N-Afr/MidEast" = "#A58AFF",
        "S-Asia" = "#00B6EB",
        "SE-Asia/E-Asia/Oceania" = "#C49A00",
        "Sub-Saharan Afr" = "#F8766D")
  )

plot1 <- ggplot(GM@data, aes(y = log_pm25, x = log_sat)) +
  geom_point(aes(color = super_region_name), alpha = 0.4, size = rel(0.8)) +
  region_color_manual +
  geom_smooth(method = glm, formula=y~poly(x, 2), color = "black", size = 0.5, linetype = 2) +
  coord_equal() +
  xylabs +
  guides(color = guide_legend(
    title = NULL,
    override.aes = list(alpha = 1, size = 2)
  )) +
  theme(legend.text = element_text(size = rel(0.6)))
plot(plot1)
```

Model used in the paper
```{r}
xylabs <- labs(
  x = expression(log(satellite)), 
  y = expression(log(PM[2.5]))
)
region_color_manual = scale_color_manual(
    values = 
      c("E-Eur/C-Eur/C-Asia" = "#00C094",
        "HighIncome" = "#FB61D7",
        "LatAm/Carib" = "#53B400",
        "N-Afr/MidEast" = "#A58AFF",
        "S-Asia" = "#00B6EB",
        "SE-Asia/E-Asia/Oceania" = "#C49A00",
        "Sub-Saharan Afr" = "#F8766D")
  )

plot1 <- ggplot(GM@data, aes(y = log_pm25, x = log_sat)) +
  geom_point(aes(color = super_region_name), alpha = 0.4, size = rel(0.8)) +
  region_color_manual +
  geom_smooth(method = lm, color = "black", size = 0.5, linetype = 2) +
  coord_equal() +
  xylabs +
  guides(color = guide_legend(
    title = NULL,
    override.aes = list(alpha = 1, size = 2)
  )) +
  theme(legend.text = element_text(size = rel(0.6)))
plot(plot1)
```

```{r}
r1 <- subset(GM@data, super_region==1)
r2 <- subset(GM@data, super_region==2)
r3 <- subset(GM@data, super_region==3)
r4 <- subset(GM@data, super_region==4)
r5 <- subset(GM@data, super_region==5)
r6 <- subset(GM@data, super_region==6)
r7 <- subset(GM@data, super_region==7)
```

```{r}
b1 <- lm(r1$log_pm25 ~ r1$log_sat)
print(b1$coefficients)
b2 <- lm(r2$log_pm25 ~ r2$log_sat)
print(b2$coefficients)
b3 <- lm(r3$log_pm25 ~ r3$log_sat)
print(b3$coefficients)
b4 <- lm(r4$log_pm25 ~ r4$log_sat)
print(b4$coefficients)
b5 <- lm(r5$log_pm25 ~ r5$log_sat)
print(b5$coefficients)
b6 <- lm(r6$log_pm25 ~ r6$log_sat)
print(b6$coefficients)
b7 <- lm(r7$log_pm25 ~ r7$log_sat)
print(b7$coefficients)
```

```{r}
plot_r1 <- ggplot(r3, aes(y = log_pm25, x = log_sat)) +
  geom_point(aes(colour = super_region_name), alpha = 0.2, size = rel(0.75)) + 
  region_color_manual +
  geom_smooth(aes(colour = super_region_name), method = lm) + 
  coord_equal() +
  xylabs + 
  legend_none() 

plot(plot_r1)
```

Same plot as above + separate fits for each super-region
```{r}
plot2 <- ggplot(GM@data, aes(y = log_pm25, x = log_sat)) +
  geom_point(aes(colour = super_region_name), alpha = 0.2, size = rel(0.75)) + 
  geom_smooth(method = lm, color = "black", size = 0.5, linetype = 2) + 
  region_color_manual +
  geom_smooth(aes(colour = super_region_name), method = lm) + 
  coord_equal() +
  xylabs + 
  legend_none() 

plot(plot2)
```


Derive labels from the data based on average PM2.5 concentration in each country
```{r}
average <- 
  GM@data %>% 
  group_by(iso3) %>% 
  summarise(pm25 = mean(pm25))
d <- dist(average)
hh <- hclust(d)
clust <- cutree(hh,k = 6)
GM@data$cluster_region <-
  sapply(GM@data$iso3, function(x) clust[which(average$iso3 == x)])

print(table(GM@data$cluster_region))

cluster_color_manual = scale_color_manual(
    values = 
      c("4" = "#00C094",
        "1" = "#FB61D7", 
        "5" = "#53B400",
        "2" = "#A58AFF",
        "3" = "#00B6EB",
        "6" = "#C49A00",
        "7" = "#F8766D")
  )
```


```{r}
plot3 <-
  ggplot(GM@data, aes(
    y = log_pm25,
    x = log_sat
  )) + 
  geom_point(aes(colour = as.factor(cluster_region)), alpha = 0.2, size = rel(0.75)) + 
  cluster_color_manual +
  geom_smooth(
    method = lm, 
    color = "black", 
    size = 0.5, 
    linetype = 2
  ) + 
  geom_smooth(aes(colour = as.factor(cluster_region)), method = lm) + 
  coord_equal() +
  guides(color = guide_legend(
    title = NULL,
    override.aes = list(alpha = 1, size = 2)
  )) +
  xylabs

plot(plot3)
```

```{r}
# Prior predictive simulations --------------------------------------------

# Inverse Gamme distribution. 
library(gridExtra)
library(grid)
library(invgamma)
library(ggplot2); theme_set(theme_bw())
x <- seq(0, 500, .01)
qplot(x, dinvgamma(x, 1, 1), geom = "line") -> plot1
qplot(x, dinvgamma(x, 1, 100), geom = "line") -> plot2
#qplot(x, 1/dinvgamma(x, 1, 1), geom = "line") -> plot3
#qplot(x, 1/dinvgamma(x, 1, 100), geom = "line") -> plot4
#qplot(x, dinvgamma(x, 2, 100), geom = "line") -> plot3
#qplot(x, dinvgamma(x, 4, 100), geom = "line") -> plot4

grid.arrange(plot1, plot2, ncol = 2)
```
```{r}
# Plot: prior predictive with vague priors
set.seed(seed = 1923840483)

tau0 <- 1 / sqrt(rgamma(1, 1, rate = 100))
tau1 <- 1 / sqrt(rgamma(1, 1, rate = 100))
sigma <- 1 / sqrt(rgamma(1, 1, rate = 100))
beta0i <- rnorm(8, 0, tau0)
beta1i <- rnorm(8, 0, tau1)
beta0 <- rnorm(1, 0, 100)
beta1 <- rnorm(1, 0, 100)

Nsim <- length(GM@data$super_region)
xysim_labs <- labs(
  x = expression(paste("Observed ", log(PM[2.5]))),
  y = "Simulated data (log scale)"
)

data1 <- data.frame(
  log_pm25 = GM$log_pm25,
  sim = beta0 + beta0i[GM$super_region] +
    (beta1 + beta1i[GM$super_region]) * GM$log_sat +
    rnorm(Nsim, mean = 0, sd = sigma)
)

theme_set(bayesplot::theme_default(base_size = 18))
theme_update(axis.text = element_text(size = 20))

ggplot(data1, aes(x = log_pm25, y = sim)) + 
  geom_point(alpha = 0.1, color = "red") + 
  xysim_labs + ggtitle("Vague Recommended Priors")
ggsave(filename = "plots/prior_pred_vague.png", width = 4.5, height = 3.75)
```

```{r}
# Plot: prior predictive with weakly informative priors
set.seed(seed = 1923840479)
tau0 <- abs(rnorm(1, 0, 1))
tau1 <- abs(rnorm(1, 0, 1))
sigma <- abs(rnorm(1, 0, 1))
beta0i <- rnorm(8, 0, tau0)
beta1i <- rnorm(8, 0, tau1)
beta0 <- rnorm(1, 0, 1)
beta1 <- rnorm(1, 1, 1)

data2 <- data.frame(
  log_pm25 = GM$log_pm25,
  sim = beta0 + beta0i[GM$super_region] +
    (beta1 + beta1i[GM$super_region]) * GM$log_sat +
    rnorm(Nsim, mean = 0, sd = sigma)
)

ggplot(data2, aes(x = log_pm25, y = sim)) +
  geom_point(alpha = 0.1) + 
  xysim_labs + ggtitle("Weakly informative joint prior data generating process")
ggsave(filename = "plots/prior_pred_wip.png", width = 4.5, height = 3.75)
```

```{r}
# Plot: prior predictive with weakly informative priors (no seed)
tau0 <- abs(rnorm(1, 0, 1))
tau1 <- abs(rnorm(1, 0, 1))
sigma <- abs(rnorm(1, 0, 1))
beta0i <- rnorm(8, 0, tau0)
beta1i <- rnorm(8, 0, tau1)
beta0 <- rnorm(1, 0, 1)
beta1 <- rnorm(1, 1, 1)

data2 <- data.frame(
  log_pm25 = GM$log_pm25,
  sim = beta0 + beta0i[GM$super_region] +
    (beta1 + beta1i[GM$super_region]) * GM$log_sat +
    rnorm(Nsim, mean = 0, sd = sigma)
)

ggplot(data2, aes(x = log_pm25, y = sim)) +
  geom_point(alpha = 0.1) + 
  xysim_labs
ggsave(filename = "plots/prior_pred_wip.png", width = 4.5, height = 3.75)
```


```{r}
# Plot: prior predictive comparison
data3 <- data.frame(
  log_pm25 = GM$log_pm25, 
  wip = data2$sim, 
  vague = data1$sim
)
ggplot(data3, aes(x=log_pm25, y=wip)) + 
  geom_point(alpha = 0.1) + 
  geom_point(
    aes(y = vague), 
    color = "red", 
    alpha = 0.1
  ) + 
  xysim_labs
ggsave(filename = "plots/prior_pred_compare.png", width = 4.5, height = 3.75)
```

```{r}
# Fit Stan models 1, 2, 3 -------------------------------------------------

# Compile Stan programs
# * simple.stan: simple linear regression (Model 1)
# * hier.stan: non-centered parameterization of hierarchical model (Model 2, Model 3)
simple_mod <- stan_model("stan/simple.stan")
hier_mod <- stan_model("stan/hierarchical.stan")

# Data for model 1
standata1 <- with(GM@data, list(
  N = length(log_pm25),
  log_pm = log_pm25,
  log_sat = log_sat
))

# Data for model 2 (using super-regions from WHO)
standata2 <- with(GM@data, list(
  N = length(log_pm25),
  R = length(unique(super_region)),
  log_pm = log_pm25,
  log_sat = log_sat,
  region = super_region
))

# Data for model 3 (using super-regions from clustering)
standata3 <- standata2
standata3$R <- length(unique(GM$cluster_region))
standata3$region <- GM$cluster_region

# Fit the models with Stan
nuts_controls <- list(max_treedepth = 15, adapt_delta = 0.99)
mod1 <- sampling(simple_mod, data = standata1, seed = 2402)
mod2 <- sampling(hier_mod, data = standata2, control = nuts_controls, seed = 2402)
mod3_diverge <- sampling(hier_mod, data = standata3, control = nuts_controls[1], seed = 2402)
mod3 <- sampling(hier_mod, data = standata3, control = nuts_controls, seed = 2402)

save(file = "stan/stanfits.RData", mod1, mod2, mod3, mod3_diverge)

# Extract parameter estimates, pointwise log-lik, and posterior predictive draws
keep_pars <- c("sigma", "beta0", "beta1", "beta0_region", "beta1_region", "tau0", "tau1")
posterior1 <- as.array(mod1, pars = keep_pars[1:3])
posterior2 <- as.array(mod2, pars = keep_pars)
posterior3_diverge <- as.array(mod3_diverge, pars = keep_pars)
posterior3 <- as.array(mod3, pars = keep_pars)
```

```{r}
# Graphical posterior predictive checks -----------------------------------
theme_set(bayesplot::theme_default(base_size = 14))
y <- standata2$log_pm
yrep1 <- as.matrix(mod1, pars = "log_pm_rep")
yrep2 <- as.matrix(mod2, pars = "log_pm_rep")
yrep3 <- as.matrix(mod3, pars = "log_pm_rep")

samp100 <- sample(nrow(yrep1), 100)

# overlaid densities
color_scheme_set("blue")
ppc_dens_overlay(y, yrep1[samp100, ]) + 
  coord_cartesian(ylim = c(0, 0.7), xlim = c(0, 6)) +
  legend_none()
ggsave(filename = "plots/ppc_dens1.png", width = 4.5, height = 3.75)

color_scheme_set("gray")
ppc_dens_overlay(y, yrep2[samp100, ]) + 
  coord_cartesian(ylim = c(0, 0.7), xlim = c(0, 6)) +
  legend_none()
ggsave(filename = "plots/ppc_dens2.png", width = 4.5, height = 3.75)

color_scheme_set("red")
ppc_dens_overlay(y, yrep3[samp100, ]) + 
  coord_cartesian(ylim = c(0, 0.7), xlim = c(0, 6)) +
  legend_none()
ggsave(filename = "plots/ppc_dens3.png", width = 4.5, height = 3.75)
```

```{r}
# stat: skew 
skew <- function(x) {
  xdev <- x - mean(x)
  n <- length(x)
  r <- sum(xdev^3) / sum(xdev^2)^1.5
  return(r * sqrt(n) * (1 - 1/n)^1.5)
}

color_scheme_set("blue")
ppc_stat(y, yrep1, stat = "skew", binwidth = 0.01) + 
  xlim(0, .6) + 
  legend_none()
ggsave(filename = "plots/ppc_skew1.png", width = 4.5, height = 3.75)

color_scheme_set("gray")
ppc_stat(y, yrep2, stat = "skew", binwidth = 0.01) + 
  xlim(0, .6) + 
  legend_none()
ggsave(filename = "plots/ppc_skew2.png", width = 4.5, height = 3.75)

color_scheme_set("red")
ppc_stat(y, yrep3, stat = "skew", binwidth = 0.01) + 
  xlim(0, .6) + 
  legend_none()
ggsave(filename = "plots/ppc_skew3.png", width = 4.5, height = 3.75)
```

```{r}
# stat: group medians
superregion <- GM@data$super_region_name
superregion <- factor(superregion, levels = levels(superregion)[c(2,1,3:7)])

color_scheme_set("blue")
ppc_stat_grouped(y, yrep1, 
                 group = superregion, 
                 stat = "median", 
                 facet_args = list(nrow = 2)) + 
  facet_text(size = rel(0.7)) + 
  scale_x_continuous(breaks = function(x) pretty(x, n = 3)) +
  legend_none()
ggsave(filename = "plots/ppc_med_grouped1.png", height = 3, width = 7)

color_scheme_set("gray")
ppc_stat_grouped(y, yrep2, 
                 group = superregion, 
                 stat = "median",
                 facet_args = list(nrow = 2)) +
  facet_text(size = rel(0.7)) + 
  scale_x_continuous(breaks = function(x) pretty(x, n = 3)) +
  legend_none()
ggsave(filename = "plots/ppc_med_grouped2.png", height = 3, width = 7)

color_scheme_set("red")
ppc_stat_grouped(y, yrep3, 
                 group = standata3$region,
                 stat = "median", 
                 facet_args = list(nrow = 2)) + 
  facet_text(size = rel(0.7)) + 
  scale_x_continuous(breaks = function(x) pretty(x, n = 3)) +
  legend_none()
ggsave(filename = "plots/ppc_med_grouped3.png", height = 3, width = 7)
```

